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Prompt Expansion for Adaptive Text-to-Image Generation

Prompt Expansion for Adaptive Text-to-Image Generation

来源:Arxiv_logoArxiv
英文摘要

Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes a Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, generates a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.

Deepak Ramachandran、Alexander Ku、Siddhartha Datta、Peter Anderson

计算技术、计算机技术

Deepak Ramachandran,Alexander Ku,Siddhartha Datta,Peter Anderson.Prompt Expansion for Adaptive Text-to-Image Generation[EB/OL].(2023-12-27)[2025-06-29].https://arxiv.org/abs/2312.16720.点此复制

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